In [1]:
from openpiv import tools, process, validation, filters, scaling 

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import imageio
In [2]:
frame_a  = tools.imread( '~/Downloads/bridgepile_wake/frame0001.tif' )
frame_b  = tools.imread( '~/Downloads/bridgepile_wake/frame0011.tif' )
In [3]:
fig,ax = plt.subplots(1,2,figsize=(12,10))
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)
Out[3]:
<matplotlib.image.AxesImage at 0x11f99cad0>
In [4]:
winsize = 48 # pixels
searchsize = 96  # pixels, search in image B
overlap = 24 # pixels
dt = 1./30 # sec, assume 30 fps


u0, v0, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak' )
In [5]:
x, y = process.get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap )
In [6]:
u1, v1, mask = validation.sig2noise_val( u0, v0, sig2noise, threshold = 1.15)
In [7]:
u2, v2 = filters.replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2)
In [8]:
x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor = 1. )
In [9]:
tools.save(x, y, u3, v3, mask, 'exp1_001.txt' )
In [10]:
# tools.display_vector_field('exp1_001.txt', scaling_factor=100., width=0.0025)
In [11]:
# If you need a larger view:
fig, ax = plt.subplots(figsize=(12,12))
tools.display_vector_field('exp1_001.txt', ax=ax, scaling_factor=1.0, scale=3500, width=0.0045, on_img=True, image_name='~/Downloads/bridgepile_wake/frame0001.tif');